The diagnosis of tuberculosis (TB) (both pulmonary and disseminated forms) in children is extremely difficult as current tests rely on culture of the causative bacteria from sputum or gastric aspirates. Culture of Mycobacterium tuberculosis may take several weeks and obtaining appropriate samples from young children is difficult. Even with the best available current methods a definitive diagnosis of childhood TB is only achieved in 20-30% of children clinically diagnosed as having TB. Lack of accurate and rapid diagnostic tests results in delayed treatment for many children, and conversely over-treatment of children who may not actually have TB is also common. There is thus an urgent need for improved diagnostic tests for childhood TB. As an alternative to detecting the causative Mycobacterium, identification of changes in blood proteins or the pattern of activation of genes in blood cells (protein or gene signatures or biomarkers) is a promising method for diagnosing many infections. The members of our consortium have previously studied well-characterised large groups of children with TB, and a range of other infections with similar symptoms to childhood TB. We have identified candidate protein and gene ?signatures? which may be useful in the diagnosis of childhood TB. Our proposal is to take forward six promising protein and gene signatures (three based on proteins and three based on changes in expressed genes) for further validation in well established cohorts of children with suspected TB in four African countries which have high burdens of childhood TB (South Africa, Malawi, Kenya and The Gambia). Using available samples from over 4,000 well characterised child TB suspects, each of the six candidate biomarkers will be validated first using the same technology as used to detect the original biomarker and then using simpler technology which enables large numbers of patients to be analysed. In order to ensure that only the most accurate and reproducible biomarkers are taken forward, we will validate each biomarker in at least three different country cohorts. We will use sophisticated statistical methodology to select the most accurate biomarkers which can be taken forward for development as tests for clinical use. In order to translate promising biomarkers to clinical tests which can be applied even in resource poor settings we will use novel technology to detect the protein and gene signatures which will be validated as the basis of a diagnostic test.